I was trained as a physicist at ETH Zurich before joining INI for a PhD. I have always been interested in the interdisciplinary field of neural computation, where biology, physics, mathematics, engineering, and computer science meet.
I work on neuro-inspired models of computation and computing architectures, both theoretically and by developing new kinds of computing hardware.
Specifically, my work focuses on
> Ultra-efficient deep neural network implementations based on analog VLSI technology,
> Event-based and probabilistic computation in recurrent neural assemblies,
> Spike-based neural computation,
> Learning and self-organization of neural circuitry.